Learning to Create Cartoon Images from a Very Small Dataset
Hsu-Yung Cheng, Chih-Chang Yu · 2019
This paper proposes a framework to automatically create cartoon images with low computation resources and small training datasets. The system segments and reassembles regions according to the topologies learned from example images. Region relationship trees are constructed for training images with no requirement of manual labeling. An enhanced clustering mechanism with no prior knowledge of cluster number is designed to effectively group components into desired groups for image creation. Compared with methods based on Generative Adversarial Networks, the proposed framework which performs automatic reasoning, clustering and reassembling regions of cartoon images can create better images with a very small amount of training samples.